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RichardErkhov/
MaziyarPanahi_-_calme-2.1-phi3.5-4b-gguf

Name: MaziyarPanahi_-_calme-2.1-phi3.5-4b-gguf
Availability: InStock

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Quantization made by Richard Erkhov.

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calme-2.1-phi3.5-4b - GGUF

Model creator: https://huggingface.co/MaziyarPanahi/
Original model: https://huggingface.co/MaziyarPanahi/calme-2.1-phi3.5-4b/

Name	Quant method	Size
calme-2.1-phi3.5-4b.Q2_K.gguf	Q2_K	1.32GB
calme-2.1-phi3.5-4b.IQ3_XS.gguf	IQ3_XS	1.51GB
calme-2.1-phi3.5-4b.IQ3_S.gguf	IQ3_S	1.57GB
calme-2.1-phi3.5-4b.Q3_K_S.gguf	Q3_K_S	1.57GB
calme-2.1-phi3.5-4b.IQ3_M.gguf	IQ3_M	1.73GB
calme-2.1-phi3.5-4b.Q3_K.gguf	Q3_K	1.82GB
calme-2.1-phi3.5-4b.Q3_K_M.gguf	Q3_K_M	1.82GB
calme-2.1-phi3.5-4b.Q3_K_L.gguf	Q3_K_L	1.94GB
calme-2.1-phi3.5-4b.IQ4_XS.gguf	IQ4_XS	1.93GB
calme-2.1-phi3.5-4b.Q4_0.gguf	Q4_0	2.03GB
calme-2.1-phi3.5-4b.IQ4_NL.gguf	IQ4_NL	2.04GB
calme-2.1-phi3.5-4b.Q4_K_S.gguf	Q4_K_S	2.04GB
calme-2.1-phi3.5-4b.Q4_K.gguf	Q4_K	2.23GB
calme-2.1-phi3.5-4b.Q4_K_M.gguf	Q4_K_M	2.23GB
calme-2.1-phi3.5-4b.Q4_1.gguf	Q4_1	2.24GB
calme-2.1-phi3.5-4b.Q5_0.gguf	Q5_0	2.46GB
calme-2.1-phi3.5-4b.Q5_K_S.gguf	Q5_K_S	2.46GB
calme-2.1-phi3.5-4b.Q5_K.gguf	Q5_K	2.62GB
calme-2.1-phi3.5-4b.Q5_K_M.gguf	Q5_K_M	2.62GB
calme-2.1-phi3.5-4b.Q5_1.gguf	Q5_1	2.68GB
calme-2.1-phi3.5-4b.Q6_K.gguf	Q6_K	2.92GB
calme-2.1-phi3.5-4b.Q8_0.gguf	Q8_0	3.78GB

Original model description:

language:

en license: mit library_name: transformers tags:
chat
phi
phi3
phi3.5
finetune base_model: microsoft/Phi-3.5-mini-instruct datasets:
MaziyarPanahi/truthy-dpo-v0.1-axolotl model_name: calme-2.1-phi3.5-4b pipeline_tag: text-generation inference: false model_creator: MaziyarPanahi quantized_by: MaziyarPanahi model-index:
name: calme-2.1-phi3.5-4b results:
- task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics:
  - type: inst_level_strict_acc and prompt_level_strict_acc value: 56.59 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.1-phi3.5-4b name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics:
  - type: acc_norm value: 36.11 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.1-phi3.5-4b name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics:
  - type: exact_match value: 14.43 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.1-phi3.5-4b name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics:
  - type: acc_norm value: 12.53 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.1-phi3.5-4b name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics:
  - type: acc_norm value: 9.77 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.1-phi3.5-4b name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics:
  - type: acc value: 32.61 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.1-phi3.5-4b name: Open LLM Leaderboard

MaziyarPanahi/calme-2.1-phi3.5-4b

This model is a fine-tuned version of the microsoft/Phi-3.5-mini-instruct, pushing the boundaries of natural language understanding and generation even further. My goal was to create a versatile and robust model that excels across a wide range of benchmarks and real-world applications.

Use Cases

This model is suitable for a wide range of applications, including but not limited to:

Advanced question-answering systems
Intelligent chatbots and virtual assistants
Content generation and summarization
Code generation and analysis
Complex problem-solving and decision support

⚡ Quantized GGUF

Here are the quants: calme-2.1-phi3.5-4b-GGUF

🏆 Open LLM Leaderboard Evaluation Results

Coming soon!

Prompt Template

This model uses ChatML prompt template:

<|system|>
You are a helpful assistant.<|end|>
<|user|>
How to explain Internet for a medieval knight?<|end|>
<|assistant|>

How to use

python

# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="MaziyarPanahi/calme-2.1-phi3.5-4b")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-2.1-phi3.5-4b")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.1-phi3.5-4b")

Ethical Considerations

As with any large language model, users should be aware of potential biases and limitations. We recommend implementing appropriate safeguards and human oversight when deploying this model in production environments.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	27.01
IFEval (0-Shot)	56.59
BBH (3-Shot)	36.11
MATH Lvl 5 (4-Shot)	14.43
GPQA (0-shot)	12.53
MuSR (0-shot)	9.77
MMLU-PRO (5-shot)	32.61

RichardErkhov/MaziyarPanahi_-_calme-2.1-phi3.5-4b-gguf